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Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWD FA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution encompassing data aggregation, incentive design, and privacy protection. Specifically, inspired by FA, CROWD FA initiates an MCS computing paradigm that enables data aggregation and incentive design. Participants can perform aggregation operations on their local data, facilitated by CROWD FA, which supports various common data aggregation operations and bidding incentives. To address privacy concerns, CROWD FA relies solely on an efficient cryptographic primitive known as additive secret sharing to simultaneously achieve privacy-preserving data aggregation and privacy-preserving incentive. To instantiate CROWD FA, this paper presents a privacy-preserving data aggregation scheme (PRADA) based on CROWD FA, capable of supporting a range of data aggregation operations. Additionally, a CROWD FA-based privacy-preserving incentive mechanism (PRAED) is designed to ensure truthful and fair incentives for each participant, while maximizing their individual rewards. Theoretical analysis and experimental evaluations demonstrate that CROWD FA protects participants’ data and bid privacy while effectively aggregating sensing data. Notably, CROWD FA outperforms state-of-the-art approaches by achieving up to 22 times faster computation time. [ABSTRACT FROM AUTHOR]

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning paradigm that aggregates user-trained local gradients into a federated model through a cryptographic protocol. Unfortunately, PPFL is vulnerable to model poisoning attacks launched by a Byzantine adversary, who crafts malicious local gradients to harm the accuracy of the federated model. To resist model poisoning attacks, existing defense strategies focus on identifying suspicious local gradients over plaintexts. However, the Byzantine adversary submits encrypted poisonous gradients to circumvent existing defense strategies in PPFL, resulting in encrypted model poisoning. To address the issue, in this paper we design a privacy-preserving defense strategy using two-trapdoor homomorphic encryption (referred to as ShieldFL), which can resist encrypted model poisoning without compromising privacy in PPFL. Specially, we first present the secure cosine similarity method aiming to measure the distance between two encrypted gradients. Then, we propose the Byzantine-tolerance aggregation using cosine similarity, which can achieve robustness for both Independently Identically Distribution (IID) and non-IID data. Extensive evaluations on three benchmark datasets (i.e., MNIST, KDDCup99, and Amazon) show that ShieldFL outperforms existing defense strategies. Especially, ShieldFL can achieve 30%-80% accuracy improvement to defend two state-of-the-art model poisoning attacks in both non-IID and IID settings. [ABSTRACT FROM AUTHOR]

With the boom of Augmented Reality (AR) and Virtual Reality (VR) applications, head-mounted smart wearable glass devices are becoming popular to help users access various services like E-mail freely. However, most existing password entry schemes on smart glasses rely on additional computers or mobile devices connected to smart glasses, which require users to switch between different systems and devices. This may greatly lower the practicability and usability of smart glasses. In this paper, we focus on this challenge and design three practical anti-eavesdropping password entry schemes on stand-alone smart glasses, named gTapper, gRotator and gTalker. The main idea is to break the correlation between the underlying password and the interaction observable to adversaries. In our IRB-approved user study, these schemes are found to be easy-to-use without additional hardware under various test conditions, where the participants can enter their passwords within moderate time, at high accuracy, and in various situations. [ABSTRACT FROM AUTHOR]

The industrial Internet-of-things (IIoT) can be seen as the usage of Internet-of-things technologies in industries, which provides a way to improve the operational efficiency. An attribute-based signature (ABS) has been a very useful technique for services requiring anonymous authentication in practice, where a signer can sign a message over a set of attributes without disclosing any information about his/her identity, and a signature only attests to the fact that it is created by a signer with several attributes satisfying some claim predicate. However, an ABS scheme requires exponentiation and/or pairing operations in the signature generation and verification algorithms, and hence, it is quite expensive for resource-constrained devices like a sensor in the IIoT network to run an ABS scheme. To reduce the computational overheads for both signers and verifiers, it has been suggested to introduce a server to help with signature generation and verification, but existing results on the ABS with “server-aided computation” either suffer from the security issues or are not sufficiently efficient. In this paper, we consider server-aided ABS one step further, and propose a notion called server-aided ABS with revocation (SA-ABSR), which not only securely mitigates the workloads of users in generating and verifying signatures, but also enables user revocation by having the server immediately stop signature generations for revoked signers. We formally define the security model for SA-ABSR, present a concrete construction of SA-ABSR based on a standard ABS scheme, and prove its security under the defined security model. Also, we implement the proposed SA-ABSR scheme and the underlying standard ABS scheme to evaluate the performance, from which it is easy to see that the proposed SA-ABSR scheme is more efficient than its underlying ABS scheme. [ABSTRACT FROM AUTHOR]

Path authentication thwarts counterfeits in RFID-based supply chains. Its motivation is that tagged products taking invalid paths are likely faked and injected by adversaries at certain supply chain partners/steps. Existing solutions are path-grained in that they simply regard a product as genuine if it takes any valid path. Furthermore, they enforce distributed authentication by offloading the sets of valid paths to some or all steps from a centralized issuer. This not only imposes network and storage overhead but also leaks transaction privacy. We present StepAuth, the first step-grained path authentication protocol that is practically efficient for authenticating products with strict path bindings. We encode a path into a secret with minimum path visibility disclosure between adjacent steps. Carrying the secret, a product has to go through steps in the exact order as in the designated path to pass authentication. StepAuth enforces no tag computation and enables each step to locally verify path secrets without pre-offloaded valid-path sets. Toward an even higher security guarantee, StepAuth can hinder an adversary capable of compromising all steps from forging valid secrets. We make StepAuth practically efficient by taking advantage of nested encryption and hybrid encryption. To achieve a 128-b security for a practically long path of 100 steps, StepAuth generates a secret around 10 KB, which can be well supported by high-memory EPC Gen2 tags. Such secrets take StepAuth less than 1 s to encode and around 10 ms to verify. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new efficient privacy-preserving outsourced computation framework over public data, called EPOC. EPOC allows a user to outsource the computation of a function over multi-dimensional public data to the cloud while protecting the privacy of the function and its output. Specifically, we introduce three types of EPOC in order to tradeoff different levels of privacy protection and performance. We present a new cryptosystem called Switchable Homomorphic Encryption with Partially Decryption (SHED) as the core cryptographic primitive for EPOC. We introduce two coding techniques, called message pre-coding technique and message extending and coding technique respectively, for messages encrypted under a composite order group. Furthermore, we propose a Secure Exponent Calculation Protocol with Public Base (SEPB), which serves as the core sub-protocol in EPOC. Detailed security analysis shows that the proposed EPOC achieves the goal of outsourcing computation of a private function over public data without privacy leakage to unauthorized parties. In addition, performance evaluations via extensive simulations demonstrate that EPOC is efficient in both computation and communications. [ABSTRACT FROM PUBLISHER]